{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the Dataset: (287136, 15)\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                     lat         lon  StarkeyTime    GMDate  \\\ntraj_id   DateTime                                                            \n880109D01 1995-04-13 13:40:06  45.239682 -118.533204    229902006  21:40:06   \n          1995-04-15 12:16:15  45.250521 -118.530438    230069775  20:16:15   \n          1995-04-15 21:39:38  45.247943 -118.541455    230103578  05:39:38   \n          1995-04-16 03:32:14  45.247429 -118.539530    230124734  11:32:14   \n          1995-04-16 04:08:28  45.247117 -118.542579    230126908  12:08:28   \n\n                                 GMTime   LocDate   LocTime  RadNum Species  \\\ntraj_id   DateTime                                                            \n880109D01 1995-04-13 13:40:06  19950413  19950413  13:40:06     409       D   \n          1995-04-15 12:16:15  19950415  19950415  12:16:15     409       D   \n          1995-04-15 21:39:38  19950416  19950415  21:39:38     409       D   \n          1995-04-16 03:32:14  19950416  19950416  03:32:14     409       D   \n          1995-04-16 04:08:28  19950416  19950416  04:08:28     409       D   \n\n                                 UTME     UTMN  Year  Grensunr  Grensuns  \\\ntraj_id   DateTime                                                         \n880109D01 1995-04-13 13:40:06  379662  5010734    95  13:13:00  02:39:00   \n          1995-04-15 12:16:15  379895  5011927    95  13:09:00  02:41:00   \n          1995-04-15 21:39:38  379039  5011656    95  13:07:00  02:43:00   \n          1995-04-16 03:32:14  379188  5011581    95  13:07:00  02:43:00   \n          1995-04-16 04:08:28  378938  5011567    95  13:07:00  02:43:00   \n\n                               Obswt  \ntraj_id   DateTime                    \n880109D01 1995-04-13 13:40:06   1.47  \n          1995-04-15 12:16:15   1.59  \n          1995-04-15 21:39:38   1.34  \n          1995-04-16 03:32:14   1.50  \n          1995-04-16 04:08:28   1.34  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>lat</th>\n      <th>lon</th>\n      <th>StarkeyTime</th>\n      <th>GMDate</th>\n      <th>GMTime</th>\n      <th>LocDate</th>\n      <th>LocTime</th>\n      <th>RadNum</th>\n      <th>Species</th>\n      <th>UTME</th>\n      <th>UTMN</th>\n      <th>Year</th>\n      <th>Grensunr</th>\n      <th>Grensuns</th>\n      <th>Obswt</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">880109D01</th>\n      <th>1995-04-13 13:40:06</th>\n      <td>45.239682</td>\n      <td>-118.533204</td>\n      <td>229902006</td>\n      <td>21:40:06</td>\n      <td>19950413</td>\n      <td>19950413</td>\n      <td>13:40:06</td>\n      <td>409</td>\n      <td>D</td>\n      <td>379662</td>\n      <td>5010734</td>\n      <td>95</td>\n      <td>13:13:00</td>\n      <td>02:39:00</td>\n      <td>1.47</td>\n    </tr>\n    <tr>\n      <th>1995-04-15 12:16:15</th>\n      <td>45.250521</td>\n      <td>-118.530438</td>\n      <td>230069775</td>\n      <td>20:16:15</td>\n      <td>19950415</td>\n      <td>19950415</td>\n      <td>12:16:15</td>\n      <td>409</td>\n      <td>D</td>\n      <td>379895</td>\n      <td>5011927</td>\n      <td>95</td>\n      <td>13:09:00</td>\n      <td>02:41:00</td>\n      <td>1.59</td>\n    </tr>\n    <tr>\n      <th>1995-04-15 21:39:38</th>\n      <td>45.247943</td>\n      <td>-118.541455</td>\n      <td>230103578</td>\n      <td>05:39:38</td>\n      <td>19950416</td>\n      <td>19950415</td>\n      <td>21:39:38</td>\n      <td>409</td>\n      <td>D</td>\n      <td>379039</td>\n      <td>5011656</td>\n      <td>95</td>\n      <td>13:07:00</td>\n      <td>02:43:00</td>\n      <td>1.34</td>\n    </tr>\n    <tr>\n      <th>1995-04-16 03:32:14</th>\n      <td>45.247429</td>\n      <td>-118.539530</td>\n      <td>230124734</td>\n      <td>11:32:14</td>\n      <td>19950416</td>\n      <td>19950416</td>\n      <td>03:32:14</td>\n      <td>409</td>\n      <td>D</td>\n      <td>379188</td>\n      <td>5011581</td>\n      <td>95</td>\n      <td>13:07:00</td>\n      <td>02:43:00</td>\n      <td>1.50</td>\n    </tr>\n    <tr>\n      <th>1995-04-16 04:08:28</th>\n      <td>45.247117</td>\n      <td>-118.542579</td>\n      <td>230126908</td>\n      <td>12:08:28</td>\n      <td>19950416</td>\n      <td>19950416</td>\n      <td>04:08:28</td>\n      <td>409</td>\n      <td>D</td>\n      <td>378938</td>\n      <td>5011567</td>\n      <td>95</td>\n      <td>13:07:00</td>\n      <td>02:43:00</td>\n      <td>1.34</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "from ptrail.core.TrajectoryDF import PTRAILDataFrame\n",
    "from ptrail.features.kinematic_features import KinematicFeatures\n",
    "from ptrail.features.temporal_features import TemporalFeatures\n",
    "from ptrail.preprocessing.interpolation import Interpolation\n",
    "from ptrail.preprocessing.filters import Filters\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import ptrail.utilities.constants as const\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "\n",
    "pdf = pd.read_csv('./data/starkey.csv')\n",
    "starkey = PTRAILDataFrame(data_set=pdf, latitude='lat', longitude='lon', datetime='DateTime',traj_id='Id')\n",
    "print(\"Size of the Dataset: {}\".format(starkey.shape))\n",
    "starkey.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yjharanwala/Desktop/PTRAIL/ptrail/preprocessing/filters.py:762: UserWarning: If kinematic features have been generated on the dataframe, then make sure to generate them again as outlier detection drops the point from the dataframe and does not run the kinematic features again.\n",
      "  warnings.warn(\"If kinematic features have been generated on the dataframe, then make \"\n"
     ]
    }
   ],
   "source": [
    "t1 = starkey.loc[starkey.index.get_level_values(const.TRAJECTORY_ID).isin(['930429E08'])]\n",
    "t1 = PTRAILDataFrame(data_set=t1.reset_index(), latitude='lat', longitude='lon', datetime='DateTime', traj_id='Id')\n",
    "t1 = KinematicFeatures.create_distance_column(t1)\n",
    "filtered_t1 = Filters.hampel_outlier_detection(dataframe=t1, column_name='Distance')\n",
    "interpolated_t1 = Interpolation.interpolate_position(dataframe=filtered_t1, time_jump=3600*2, ip_type='cubic')\n",
    "temporal_features_t1 = TemporalFeatures.generate_temporal_features(interpolated_t1)\n",
    "kinematic_features_k1 = KinematicFeatures.generate_kinematic_features(temporal_features_t1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yjharanwala/Desktop/PTRAIL/ptrail/preprocessing/filters.py:762: UserWarning: If kinematic features have been generated on the dataframe, then make sure to generate them again as outlier detection drops the point from the dataframe and does not run the kinematic features again.\n",
      "  warnings.warn(\"If kinematic features have been generated on the dataframe, then make \"\n"
     ]
    }
   ],
   "source": [
    "t2 = starkey.loc[starkey.index.get_level_values(const.TRAJECTORY_ID).isin(['940213D01'])]\n",
    "t2 = PTRAILDataFrame(data_set=t2.reset_index(), latitude='lat', longitude='lon', datetime='DateTime', traj_id='Id')\n",
    "t2 = KinematicFeatures.create_distance_column(t2)\n",
    "filtered_t2 = Filters.hampel_outlier_detection(dataframe=t2, column_name='Distance')\n",
    "interpolated_t2 = Interpolation.interpolate_position(dataframe=filtered_t2, time_jump=3600*2, ip_type='cubic')\n",
    "temporal_features_t2 = TemporalFeatures.generate_temporal_features(interpolated_t2)\n",
    "kinematic_features_k2 = KinematicFeatures.generate_kinematic_features(temporal_features_t2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yjharanwala/Desktop/PTRAIL/ptrail/preprocessing/filters.py:762: UserWarning: If kinematic features have been generated on the dataframe, then make sure to generate them again as outlier detection drops the point from the dataframe and does not run the kinematic features again.\n",
      "  warnings.warn(\"If kinematic features have been generated on the dataframe, then make \"\n"
     ]
    }
   ],
   "source": [
    "t3 = starkey.loc[starkey.index.get_level_values(const.TRAJECTORY_ID).isin(['OSUX93110'])]\n",
    "t3 = PTRAILDataFrame(data_set=t3.reset_index(), latitude='lat', longitude='lon', datetime='DateTime', traj_id='Id')\n",
    "t3 = KinematicFeatures.create_distance_column(t3)\n",
    "filtered_t3 = Filters.hampel_outlier_detection(dataframe=t3, column_name='Distance')\n",
    "interpolated_t3 = Interpolation.interpolate_position(dataframe=filtered_t3, time_jump=3600*2, ip_type='cubic')\n",
    "temporal_features_t3 = TemporalFeatures.generate_temporal_features(interpolated_t3)\n",
    "kinematic_features_k3 = KinematicFeatures.generate_kinematic_features(temporal_features_t3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f3b9bacf9d0>]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1_coords = list(zip(t1['lat'], t1['lon']))\n",
    "ft1_coords = list(zip(filtered_t1['lat'], filtered_t1['lon']))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "for i in range(len(t1_coords)):\n",
    "    if t1_coords[i] in ft1_coords:\n",
    "        ax.scatter(t1_coords[i][0], t1_coords[i][1], color='grey')\n",
    "    else:\n",
    "        ax.scatter(t1_coords[i][0], t1_coords[i][1], color='red')\n",
    "\n",
    "ax.plot(t1['lat'], t1['lon'], color='grey')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f3ba98734c0>]"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(filtered_t1['lat'], filtered_t1['lon'], color='grey'),\n",
    "plt.plot(filtered_t1['lat'], filtered_t1['lon'], color='grey')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f3b9b978730>]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ip_coords = list(zip(interpolated_t1['lat'], interpolated_t1['lon']))\n",
    "ft1_coords = list(zip(filtered_t1['lat'], filtered_t1['lon']))\n",
    "\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot()\n",
    "for i in range(len(interpolated_t1)):\n",
    "    if ip_coords[i] in ft1_coords:\n",
    "        ax1.scatter(ip_coords[i][0], ip_coords[i][1], color='grey')\n",
    "    else:\n",
    "        ax1.scatter(ip_coords[i][0], ip_coords[i][1], color='green')\n",
    "\n",
    "ax1.plot(interpolated_t1['lat'], interpolated_t1['lon'], color='grey')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x7f3b9a0f1100>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1800x1440 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fix, ax = plt.subplots(2, 2, figsize=(25, 20))\n",
    "ax[0, 0].hist(kinematic_features_k1['Distance'], color='skyblue', label='Elk', histtype='stepfilled', linewidth=3),\n",
    "# ax[0, 0].hist(kinematic_features_k2['Distance_prev_to_curr'], color='grey', label='Deer', histtype='step', linewidth=3),\n",
    "# ax[0, 0].hist(kinematic_features_k3['Distance_prev_to_curr'], color='green', label='Cattle',  histtype='step', linewidth=2),\n",
    "ax[0, 0].axis(xmin=0, xmax=3000)\n",
    "ax[0, 0].set_title('Distance Histogram', fontsize=25)\n",
    "ax[0, 0].legend(loc='upper right', prop={'size': 25})\n",
    "\n",
    "\n",
    "ax[0, 1].hist(kinematic_features_k1['Speed'], color='skyblue', label='Elk', histtype='stepfilled', linewidth=3),\n",
    "# ax[0, 1].hist(kinematic_features_k2['Speed_prev_to_curr'], color='grey', label='Deer', histtype='step', linewidth=3),\n",
    "# ax[0, 1].hist(kinematic_features_k3['Speed_prev_to_curr'], color='green', label='Cattle', histtype='step', linewidth=2),\n",
    "ax[0, 1].axis(xmin=0, xmax=1)\n",
    "ax[0, 1].set_title('Speed Histogram', fontsize=25)\n",
    "ax[0, 1].legend(loc='upper right', prop={'size': 25})\n",
    "\n",
    "ax[1, 0].hist(kinematic_features_k1['Bearing'], color='skyblue', label='Elk', histtype='stepfilled', linewidth=3),\n",
    "# ax[1, 0].hist(kinematic_features_k2['Bearing_between_consecutive'], color='grey', label='Deer', histtype='step', linewidth=3),\n",
    "# ax[1, 0].hist(kinematic_features_k3['Bearing_between_consecutive'], color='green', label='Cattle', histtype='step', linewidth=3),\n",
    "ax[1, 0].set_title('Bearing Histogram', fontsize=25)\n",
    "ax[1, 0].legend(loc='upper right', prop={'size': 20})\n",
    "\n",
    "ax[1, 1].hist(kinematic_features_k1['Bearing_Rate'], color='skyblue', label='Elk', histtype='stepfilled', linewidth=3),\n",
    "# ax[1, 1].hist(kinematic_features_k2['Bearing_rate_from_prev'], color='grey', label='Deer', histtype='step', linewidth=3),\n",
    "# ax[1, 1].hist(kinematic_features_k3['Bearing_rate_from_prev'], color='green', label='Cattle', histtype='step', linewidth=3),\n",
    "ax[1, 1].set_title('Bearing_rate Histogram', fontsize=25)\n",
    "ax[1, 1].legend(loc='upper right', prop={'size': 25})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "f1 = starkey.loc[starkey['Species'] == 'D']\n",
    "f2 = starkey.loc[starkey['Species'] == 'E']\n",
    "f3 = starkey.loc[starkey['Species'] == 'C']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "f1 = PTRAILDataFrame(data_set=f1.reset_index(), latitude='lat', longitude='lon', traj_id='traj_id', datetime='DateTime')\n",
    "f2 = PTRAILDataFrame(data_set=f2.reset_index(), latitude='lat', longitude='lon', traj_id='traj_id', datetime='DateTime')\n",
    "f3 = PTRAILDataFrame(data_set=f3.reset_index(), latitude='lat', longitude='lon', traj_id='traj_id', datetime='DateTime')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "f1 = KinematicFeatures.create_speed_column(f1)\n",
    "f2 = KinematicFeatures.create_speed_column(f2)\n",
    "f3 = KinematicFeatures.create_speed_column(f3)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average speed of deer is: 0.08595855104608327 m/s\n",
      "The average speed of elk is: 0.14014369076099686 m/s\n",
      "The average speed of cattle is: 0.07465261520270267 m/s\n"
     ]
    }
   ],
   "source": [
    "print(f\"The average speed of deer is: {f1['Speed'].mean(skipna=True)} m/s\")\n",
    "print(f\"The average speed of elk is: {f2['Speed'].mean(skipna=True)} m/s\")\n",
    "print(f\"The average speed of cattle is: {f3['Speed'].mean(skipna=True)} m/s\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Bearing Histogram')"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(kinematic_features_k1['Bearing'], color='green', label='Elk', histtype='stepfilled', linewidth=3),\n",
    "plt.title('Bearing Histogram')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}